In this study we perform wavelet analysis on high dimensional microarray data. We perform two methods of feature extraction on microarray data, using approximation coefficients and detail coefficients. A set of orthogonal wavelet approximation coefficients based on wavelet decomposition are extracted to compress the gene profiles and reduce the dimensionality of microarray data. A set of orthogonal wavelet detail coefficients are extracted to characterize the localized features of gene profiles and reduce dimensionality. Experiments are performed on two datasets. The experimental results show a highly competitive accuracy is achieved compared to the best performance of other kinds of classification models using wavelet coefficients.